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Journal articles on the topic 'Correspondence estimation'

Author: Grafiati

Published: 25 May 2024

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1

Liu, Yizhang, Shengjie Zhao, Hao Deng, and Fuqiang Ding. "Correspondence Learning via Correspondence Embedded and Channel Recalibration Network." ITM Web of Conferences 60 (2024): 00008. http://dx.doi.org/10.1051/itmconf/20246000008.

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Correspondence learning is pivotal to many computer vision-based tasks. Existing methods regard each correspondence equally along the channel dimension, which weakens the feature representation capability of the network. To alleviate this problem, we propose a Correspondence Embedded and Channel Recalibration Network, named CECR-Net, to predict the inlier probability of each correspondence and recover camera poses. The proposed CECR-Net is designed to explore the potential impact of correspondences on the channel dimension, and recalibrate the weight of each channel, so that our CECRNet can capture more exact contextual information. Experiments show that our CECR-Net is effective in outlier removal and camera pose estimation tasks on challenging public datasets.

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Huang, Zhaoyang, Xiaokun Pan, Weihong Pan, Weikang Bian, Yan Xu, Ka Chun Cheung, Guofeng Zhang, and Hongsheng Li. "NeuralMarker." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–10. http://dx.doi.org/10.1145/3550454.3555468.

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We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Deep learning has presented an excellent performance in correspondence learning once provided with sufficient training data. However, annotating pixel-wise dense correspondence for training marker correspondence is too expensive. We observe that the challenges of marker correspondence estimation come from two individual aspects: geometry variation and appearance variation. We, therefore, design two components addressing these two challenges in NeuralMarker. First, we create a synthetic dataset FlyingMarkers containing marker-image pairs with ground truth dense correspondences. By training with FlyingMarkers, the neural network is encouraged to capture various marker motions. Second, we propose the novel Symmetric Epipolar Distance (SED) loss, which enables learning dense correspondence from posed images. Learning with the SED loss and the cross-lighting posed images collected by Structure-from-Motion (SfM), NeuralMarker is remarkably robust in harsh lighting environments and avoids synthetic image bias. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.

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Zhang, Shihua, and Jiayi Ma. "ConvMatch: Rethinking Network Design for Two-View Correspondence Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3472–79. http://dx.doi.org/10.1609/aaai.v37i3.25456.

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Multilayer perceptron (MLP) has been widely used in two-view correspondence learning for only unordered correspondences provided, and it extracts deep features from individual correspondence effectively. However, the problem of lacking context information limits its performance and hence, many extra complex blocks are designed to capture such information in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage convolutional neural network (CNN) as the backbone to capture better context, thus avoiding the complex design of extra blocks. Specifically, with the observation that sparse motion vectors and dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Extensive experiments reveal that ConvMatch with a simple CNN backbone consistently outperforms state-of-the-arts including MLP-based methods for relative pose estimation and homography estimation, and shows promising generalization ability to different datasets and descriptors. Our code is publicly available at https://github.com/SuhZhang/ConvMatch.

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Fu, Mingliang, and Weijia Zhou. "DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation." Sensors 19, no. 5 (February 28, 2019): 1032. http://dx.doi.org/10.3390/s19051032.

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In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box’s corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D–3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence–evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods.

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Dai, Luanyuan, Xin Liu, Jingtao Wang, Changcai Yang, and Riqing Chen. "Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation." Entropy 23, no. 8 (August 9, 2021): 1024. http://dx.doi.org/10.3390/e23081024.

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Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor (KNN) algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix (LNC) between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.

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Redert, A., E. Hendriks, and J. Biemond. "Correspondence estimation in image pairs." IEEE Signal Processing Magazine 16, no. 3 (May 1999): 29–46. http://dx.doi.org/10.1109/79.768571.

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YAMASHINA, Hideki, Akihiro ICHIHASHI, Atushi KURODA, and Koichi IKEDA. "ESTIMATION OF COLOR RENDERING INDICES WITH CORRESPONDENCE TO PERCEIVED COLOR SHIFTS." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 78, Appendix (1994): 377–78. http://dx.doi.org/10.2150/jieij1980.78.appendix_377.

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Pons-Moll, Gerard, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon. "Metric Regression Forests for Correspondence Estimation." International Journal of Computer Vision 113, no. 3 (April 11, 2015): 163–75. http://dx.doi.org/10.1007/s11263-015-0818-9.

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Yi, Yunai, Diya Sun, Peixin Li, Tae-Kyun Kim, Tianmin Xu, and Yuru Pei. "Unsupervised random forest for affinity estimation." Computational Visual Media 8, no. 2 (December 6, 2021): 257–72. http://dx.doi.org/10.1007/s41095-021-0241-9.

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AbstractThis paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

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Tang, Jiexiong, John Folkesson, and Patric Jensfelt. "Geometric Correspondence Network for Camera Motion Estimation." IEEE Robotics and Automation Letters 3, no. 2 (April 2018): 1010–17. http://dx.doi.org/10.1109/lra.2018.2794624.

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Han, Li, Dan Li, Shu Ning Liu, Yu Nan Liu, and Di Tang. "Similarity estimation based on sparse spectral correspondence." Multimedia Tools and Applications 78, no. 11 (November 7, 2018): 14443–63. http://dx.doi.org/10.1007/s11042-018-6623-z.

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Hödel, M., T. Koch, L. Hoegner, and U. Stilla. "MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 55–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-55-2019.

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<p><strong>Abstract.</strong> Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.</p>

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Fan, Yingruo, Jacqueline Lam, and Victor Li. "Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12701–8. http://dx.doi.org/10.1609/aaai.v34i07.6963.

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The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance. Previous approaches mainly rely on probabilistic models or predefined rules for modeling co-occurrence relationships among AUs, leading to limited generalization. In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps. In the heatmap regression-based network, feature maps preserve rich semantic information associated with AU intensities and locations. Moreover, the AU co-occurring pattern can be reflected by activating a set of feature channels, where each channel encodes a specific visual pattern of AU. This motivates us to model the correlation among feature channels, which implicitly represents the co-occurrence relationship of AU intensity levels. Specifically, we introduce a semantic correspondence convolution (SCC) module to dynamically compute the correspondences from deep and low resolution feature maps, and thus enhancing the discriminability of features. The experimental results demonstrate the effectiveness and the superior performance of our method on two benchmark datasets.

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Valeriy, Khabarov, Tesselkin Alexandr, and Kosolapov Kirill. "Design of experiments for transport correspondence matrix estimation." Proceedings of the Russian higher school Academy of sciences, no. 3 (September 21, 2015): 109–16. http://dx.doi.org/10.17212/1727-2769-2015-3-109-116.

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Besse, Frederic, Carsten Rother, Andrew Fitzgibbon, and Jan Kautz. "PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation." International Journal of Computer Vision 110, no. 1 (August 20, 2013): 2–13. http://dx.doi.org/10.1007/s11263-013-0653-9.

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Castillo, Louise IR, M. Erin Browne, Thomas Hadjistavropoulos, Kenneth M. Prkachin, and Rafik Goubran. "Automated vs. manual pain coding and heart rate estimations based on videos of older adults with and without dementia." Journal of Rehabilitation and Assistive Technologies Engineering 7 (January 2020): 205566832095019. http://dx.doi.org/10.1177/2055668320950196.

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Introduction Technological advances have allowed for the estimation of physiological indicators from video data. FaceReader™ is an automated facial analysis software that has been used widely in studies of facial expressions of emotion and was recently updated to allow for the estimation of heart rate (HR) using remote photoplethysmography (rPPG). We investigated FaceReader™-based heart rate and pain expression estimations in older adults in relation to manual coding by experts. Methods Using a video dataset of older adult patients with and without dementia, we assessed the relationship between FaceReader’s™ HR estimations against a well-established Video Magnification (VM) algorithm during baseline and pain conditions. Furthermore, we examined the correspondence between the Facial Action Coding System (FACS)-based pain scores obtained through FaceReader™ and manual coding. Results FaceReader’s™ HR estimations were correlated with VM algorithm in baseline and pain conditions. Non-verbal FaceReader™ pain scores and manual coding were also highly correlated despite discrepancies between the FaceReader™ and manual coding in the absolute value of scores based on pain-related facial action coding of the events preceding and following the pain response. Conclusions Compared to expert manual FACS coding and optimized VM algorithm, FaceReader™ showed good results in estimating HR values and non-verbal pain scores.

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Krak, Yu V., O. V. Barmak, and O. V. Mazurets. "The practice investigation of the information technology efficiency for automated definition of terms in the semantic content of educational materials." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 237–45. http://dx.doi.org/10.15407/pp2016.02-03.237.

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The information technology on base of the disperse evaluation, which with enough high efficiency allows automated define the semantic terms in content of educational materials article is given. The factors that hinder effective analysis of educational materials have been considered. High efficiency offered technologies gives possible of its using in row of the problems, such as estimation of the correspondence of educational materials to requirements, estimation of the correspondence of set test tasks to educational materials, semantic help of making tests, automated keyword list and abstract generation.

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LIU, Y., and M. A. RODRIGUES. "INVARIANT GEOMETRIC PROPERTIES OF IMAGE CORRESPONDENCE VECTORS AS RIGID CONSTRAINTS TO MOTION ESTIMATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 08 (December 1999): 1165–79. http://dx.doi.org/10.1142/s0218001499000665.

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Accurate motion estimation algorithms are based on a number of invariant properties that can be inferred from the motion. A large number of calibration algorithms have been proposed over the last two decades mainly based on analytic, perspective, or epipolar geometries. Extending Chasles' screw motion concept to the estimation of motion parameters in computer vision, we have presented an analysis of geometric properties of image correspondence vectors synthesized into a single coordinate frame and developed calibration algorithms using both simulated and real range image data.15,16 In this paper, we extend that work by defining the relevant geometric properties of image correspondence vectors from the point of view of invariants and by developing two calibration algorithms using the Monte Carlo method and median filtering. The algorithms are applied to real and synthetic image data corrupted by noise and outliers. Experimental results demonstrate that the median filter based algorithm is generally more robust and accurate than the Monte Carlo based algorithm and that the geometric analysis of invariant properties of correspondence vectors is a useful framework to motion parameter estimation.

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Shao, Huixiang, Zhijiang Zhang, Xiaoyu Feng, and Dan Zeng. "SCRnet: A Spatial Consistency Guided Network Using Contrastive Learning for Point Cloud Registration." Symmetry 14, no. 1 (January 12, 2022): 140. http://dx.doi.org/10.3390/sym14010140.

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Point cloud registration is used to find a rigid transformation from the source point cloud to the target point cloud. The main challenge in the point cloud registration is in finding correct correspondences in complex scenes that may contain many noise and repetitive structures. At present, many existing methods use outlier rejections to help the network obtain more accurate correspondences, but they often ignore the spatial consistency between keypoints. Therefore, to address this issue, we propose a spatial consistency guided network using contrastive learning for point cloud registration (SCRnet), in which its overall stage is symmetrical. SCRnet consists of four blocks, namely feature extraction block, confidence estimation block, contrastive learning block and registration block. Firstly, we use mini-PointNet to extract coarse local and global features. Secondly, we propose confidence estimation block, which formulate outlier rejection as confidence estimation problem of keypoint correspondences. In addition, the local spatial features are encoded into the confidence estimation block, which makes the correspondence possess local spatial consistency. Moreover, we propose contrastive learning block by constructing positive point pairs and hard negative point pairs and using Point-Pair-INfoNCE contrastive loss, which can further remove hard outliers through global spatial consistency. Finally, the proposed registration block selects a set of matching points with high spatial consistency and uses these matching sets to calculate multiple transformations, then the best transformation can be identified by initial alignment and Iterative Closest Point (ICP) algorithm. Extensive experiments are conducted on KITTI and nuScenes dataset, which demonstrate the high accuracy and strong robustness of SCRnet on point cloud registration task.

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Martynenko, A. V., and A. A. Shevtsov. "Estimation of the passenger correspondence matrix using the least squares method to calibrate the gravity model." Herald of the Ural State University of Railway Transport, no. 4 (2021): 4–13. http://dx.doi.org/10.20291/2079-0392-2021-4-4-13.

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The matrix of passenger correspondence plays a key role in many issues of planning and forecasting the development of the transport system. Usually, a gravitational model is used to evaluate the correspondence matrix. The specification and calibration method of the gravity model depends on what data is known from observations. In particular, if the volumes of departures and arrivals for each point and the average cost (duration) of the trip are known, then a gravitational model with an exponential gravity function is used, and the Hyman method is used to calibrate it. If the correspondence matrix is partially known, then a piecewise constant function can be taken as the gravity function and the Poisson method can be used for calibration. In this paper, we investigate the possibility of using the least squares method to calibrate a gravitational model with an exponential gravity function based on the observed correspondence matrix obtained on the basis of automated data collection on the online service of announcements about joint trips (carpooling). For such an observed correspondence matrix, a model correspondence matrix is calculated using the Huffman method and the least squares method. As a result, it was found that both methods produce models that approximate the observed data equally well.

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Fokov, O. A/. "Analysis of the state of the art in the problem of determining the pose of on-orbit service objects." Technical mechanics 2023, no. 1 (April 11, 2023): 54–67. http://dx.doi.org/10.15407/itm2023.01.054.

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Recently considerable attention has been paid to the problem of estimating the pose of an on-orbit service object. Determining the pose at a close distance still remains an open line of research, especially for non-cooperative objects (targets) of on-orbit service. The goal of this work is to overview the state of the art in the problem of determining the relative motion parameters of on-orbit service objects with emphasis on close proximity operations with non-cooperative and unknown targets. The method employed is the analysis of publications devoted to this problem over the last decade. The analysis showed the following. Determining the pose of a non-cooperative orbital object using video systems is a classical approach due to the advantages of light weight and low power consumption. Video camera based pose estimation algorithms usually require prior knowledge of the target features. The main methods of pose estimation still involve approaches based on the recognition and correspondence of image features for consecutive frames or with a target model. Another major approach to pose determination is lidar navigation, where the recognition and correspondence of features based on lidar-derived target surface point clouds are also common methods. Recently, a trend has emerged towards the development of non-feature methods for target pose determination, including unknown targets. The three-dimensional nature of lidar point cloud data is favorable for target pose estimation without any target model. As to the applicability of target pose estimation methods to an unknown target, the implementation of the obvious approach based on constructing a three-dimensional model of the target by processing a series of its images prior to estimating its spatial motion takes a lot of time, which is critical in close proximity operations. The trend in target pose estimation is the development of methods for simultaneous estimation of the pose and shape of an unknown object. In general, the case of an unknown object has not yet been fully investigated.

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Schweizer, Karl, Stefan Troche, and Siegbert Reiß. "Can Variances of Latent Variables be Scaled in Such a Way That They Correspond to Eigenvalues?" International Journal of Statistics and Probability 6, no. 6 (September 15, 2017): 35. http://dx.doi.org/10.5539/ijsp.v6n6p35.

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The paper reports an investigation of whether sums of squared factor loadings obtained in confirmatory factor analysis correspond to eigenvalues of exploratory factor analysis. The sum of squared factor loadings reflects the variance of the corresponding latent variable if the variance parameter of the confirmatory factor model is set equal to one. Hence, the computation of the sum implies a specific type of scaling of the variance. While the investigation of the theoretical foundations suggested the expected correspondence between sums of squared factor loadings and eigenvalues, the necessity of procedural specifications in the application, as for example the estimation method, revealed external influences on the outcome. A simulation study was conducted that demonstrated the possibility of exact correspondence if the same estimation method was applied. However, in the majority of realized specifications the estimates showed similar sizes but no correspondence.

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Bian, Jia-Wang, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, and Ian Reid. "GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence." International Journal of Computer Vision 128, no. 6 (December 17, 2019): 1580–93. http://dx.doi.org/10.1007/s11263-019-01280-3.

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AbstractFeature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50K correspondences are processed. This has important implications for real-time applications. What’s more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement.

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Tsai, Chi-Yi, Kuang-Jui Hsu, and Humaira Nisar. "Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms." Algorithms 11, no. 8 (August 12, 2018): 122. http://dx.doi.org/10.3390/a11080122.

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Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.

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Hama, Katsumi, and Yukinori Kakazu. "Motion Estimation of Object based on Three-Dimensional Correspondence." Journal of the Japan Society for Precision Engineering 59, no. 2 (1993): 222–26. http://dx.doi.org/10.2493/jjspe.59.222.

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Laskov, P., and C. Kambhamettu. "Curvature-based algorithms for nonrigid motion and correspondence estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 10 (October 2003): 1349–54. http://dx.doi.org/10.1109/tpami.2003.1233911.

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Li, Shaoxin, Xin Liu, Xiujuan Chai, Haihong Zhang, Shihong Lao, and Shiguang Shan. "Maximal Likelihood Correspondence Estimation for Face Recognition Across Pose." IEEE Transactions on Image Processing 23, no. 10 (October 2014): 4587–600. http://dx.doi.org/10.1109/tip.2014.2351265.

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Chen, Pengwen. "A Novel Kernel Correlation Model with the Correspondence Estimation." Journal of Mathematical Imaging and Vision 39, no. 2 (October 20, 2010): 100–120. http://dx.doi.org/10.1007/s10851-010-0230-6.

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Lin, Wen-Yan, Loong-Fah Cheong, Ping Tan, Guo Dong, and Siying Liu. "Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence." International Journal of Computer Vision 96, no. 2 (May 11, 2011): 145–61. http://dx.doi.org/10.1007/s11263-011-0456-9.

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Yang, Haiwei, Fei Wang, Zhe Li, and Hang Dong. "Simultaneous Pose and Correspondence Estimation Based on Genetic Algorithm." International Journal of Distributed Sensor Networks 11, no. 11 (January 2015): 828241. http://dx.doi.org/10.1155/2015/828241.

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Gajewski, Byron J., and Clifford H. Spiegelman. "Correspondence estimation of the source profiles in receptor modeling." Environmetrics 15, no. 6 (August 19, 2004): 613–34. http://dx.doi.org/10.1002/env.654.

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Hosseini, Morteza Darvish Morshedi, Miroslav Goljan, and Hui Zeng. "Semi-Blind Image Resampling Factor Estimation for PRNU Computation." Electronic Imaging 2020, no. 4 (January 26, 2020): 77–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.4.mwsf-077.

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Camera sensor fingerprints for digital camera forensics are formed by Photo-Response Non-Uniformity (PRNU), or more precisely, by estimating PRNU from a set of images taken with a camera. These images must be aligned with each other to establish sensor location pixel-to-pixel correspondence. If some of these images have been resized and cropped, the transformations need to be reversed. In this work we deal with estimation of resizing factor in the presence of one reference image from the same camera. For this problem we coin the term semi-blind estimation of resizing factor. We post two requirements that any solution of this problem should meet. It needs to be reasonably fast and exhibit very low estimation error. Our work shows that this problem can be solved using established image matching in Fourier-Mellin transform applied to vertical and horizontal projections of noise residuals (also called linear patterns).

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Katsulai, Hiroshi. "Extraction and Motion Estimation of 3D Objects from Stereo Image Sequence." Journal of Robotics and Mechatronics 8, no. 6 (December 20, 1996): 561–70. http://dx.doi.org/10.20965/jrm.1996.p0561.

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It is important to estimate motion parameters and structures of 3D objects from the image sequence, and a variety of methods to do so have been proposed so far. In this paper, a method is described of estimating the motion and structure of a 3D object from the stereo image sequence. In this method, based on the correspondence of 2D segments in each stereo pair, using a newly devised similarity measure, 3D segments are reconstructed, then grouped into rigid objects, making use of the correspondence of 3D segments in successive images, and parameters of rigid motions are computed for each group.

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Batra, Deepak, Sanjay Sharma, and Amit Kumar Kohli. "Improved Parameter Estimation for First-Order Markov Process." Research Letters in Signal Processing 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/186250.

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This correspondence presents a linear transformation, which is used to estimate correlation coefficient of first-order Markov process. It outperforms zero-forcing (ZF), minimum mean-squared error (MMSE), and whitened least-squares (WTLSs) estimators by controlling output noise variance at the cost of increased computational complexity.

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ELHADY, GAMAL F. "3D STRUCTURE FROM MOTION WITH FOURIER DESCRIPTOR TRANSFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 05 (August 2013): 1355006. http://dx.doi.org/10.1142/s0218001413550069.

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The simultaneous recovery of three-dimensional (3D) structure from motion (SfM) for the sequences of images, is one of the more difficult problems in computer vision. Classical approaches to the problem rely on using algebraic techniques to solve for these unknowns given two or more image. Motion analysis and 3D shape estimation based on the estimated motion is an important problem in computer vision. The correspondence problem is an important tool in SfM where in this paper a general 3D motion based on a simple rotation, tilt, roll and translation is proposed, and then is used for 3D shape estimation. The current work expands the 2D motion estimation (shift, rotation) to accommodate general 3D motion (shift, rotation, tilt and roll). The proposed work in this paper of motion estimation of moving object is based on Fourier Descriptor Transformation (FDT) analysis before and after motion. The FDT is also used to resolve the correspondence problem in a sequence of images. We test our method on several large-scale photo collections, show the efficacy of the introduced approach to improve reconstruction accuracy.

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Du, Shitong, Helge A. Lauterbach, Xuyou Li, Girum G. Demisse, Dorit Borrmann, and Andreas Nüchter. "Curvefusion—A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration." Sensors 20, no. 23 (December 3, 2020): 6918. http://dx.doi.org/10.3390/s20236918.

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Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.

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García, Bryan, Carlos Diego Ferrin B., and Jorge Humberto Erazo. "3D reconstruction system for semi-automatic estimation of objects length and area by means of stereo vision." Sistemas y Telemática 15, no. 40 (April 5, 2017): 27–39. http://dx.doi.org/10.18046/syt.v15i40.2372.

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It is mandatory to characterize dimensionally the manufactured industrial pieces for quality control purposes. As it is not possible to touch some pieces when trying to retrieve dimensional information, then non-invasive techniques are required to do so. Stereo vision is a passive technology which is both robust and accurate for non-invasive applications. For this reason, in this work we describe the design and implementation of a 3D reconstruction system for the estimation of the length and area of certain objects. This tool allows to easily incorporate new image correspondence techniques to its main execution pipeline. We carry some experiments and show certain benefits when selecting an accurate image correspondence technique for the estimation of the length and area.

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A., Deepambika V., and M. Abdul Rahiman. "Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence." Journal of Computer Science 15, no. 4 (April 1, 2019): 450–62. http://dx.doi.org/10.3844/jcssp.2019.450.462.

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Chi-Ying Tsui, J. Monteiro, M. Pedram, S. Devadas, A. M. Despain, and B. Lin. "Correction to "Power Estimation Methods for Sequential Logic Circuits" [Correspondence]." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 4, no. 4 (December 1996): 495. http://dx.doi.org/10.1109/tvlsi.1996.544414.

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Sunghwan Choi, Dongbo Min, Bumsub Ham, and Kwanghoon Sohn. "Unsupervised Texture Flow Estimation Using Appearance-Space Clustering and Correspondence." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 3652–65. http://dx.doi.org/10.1109/tip.2015.2449078.

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Jeong, Hyundoo, and Byung-Jun Yoon. "Effective Estimation of Node-to-Node Correspondence Between Different Graphs." IEEE Signal Processing Letters 22, no. 6 (June 2015): 661–65. http://dx.doi.org/10.1109/lsp.2014.2366051.

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Zhang, Feihu, Shibiao Xu, and Xiaopeng Zhang. "High accuracy correspondence field estimation via MST based patch matching." Multimedia Tools and Applications 79, no. 19-20 (January 28, 2020): 13291–309. http://dx.doi.org/10.1007/s11042-020-08633-y.

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Liu, JinBo, XiaoHu Zhang, HaiBo Liu, Yun Yuan, ZhaoKun Zhu, and QiFeng Yu. "New method for camera pose estimation based on line correspondence." Science China Technological Sciences 56, no. 11 (September 30, 2013): 2787–97. http://dx.doi.org/10.1007/s11431-013-5361-8.

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Liu, Yuncai, and Thomas S. Huang. "A linear algorithm for motion estimation using straight line correspondence." Computer Vision, Graphics, and Image Processing 43, no. 3 (September 1988): 432. http://dx.doi.org/10.1016/0734-189x(88)90093-x.

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Leng, D. W., and W. D. Sun. "Iterative three-dimensional rigid object pose estimation with contour correspondence." IET Image Processing 6, no. 5 (2012): 569. http://dx.doi.org/10.1049/iet-ipr.2010.0435.

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Li, Shenghao, Qunfei Zhao, and Zeyang Xia. "Sparse-to-Local-Dense Matching for Geometry-Guided Correspondence Estimation." IEEE Transactions on Image Processing 32 (2023): 3536–51. http://dx.doi.org/10.1109/tip.2023.3287500.

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Tomaževič, Dejan, Boštjan Likar, and Franjo Pernuš. "MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION." Image Analysis & Stereology 31, no. 1 (March 15, 2012): 43. http://dx.doi.org/10.5566/ias.v31.p43-53.

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Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

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Lebedeva, Ol'ga. "COMPARATIVE ANALYSIS OF EXISTING METHODS FOR DEVELOPING CORRESPONDENCE MATRICES." Modern Technologies and Scientific and Technological Progress 1, no. 1 (May 17, 2021): 181–82. http://dx.doi.org/10.36629/2686-9896-2021-1-1-181-182.

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The study is aimed at comparing the methods of restoring correspondence matri ces, the traditional transport school and modern mobile technologies. Traditionally, correspondence matrices are restored by interviewing vehicle drivers and applying gravity models (to extrapolate and fill in unobserved movements), then matrix estimation methods are used to calculate traffic flows. Each of these approaches has a number of advantages and disadvantages, but there is no evidence to assess the quality of the data obtained.

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LEI, B. J., LI-QUN XU, E. A. HENDRIKS, and J. BIEMOND. "ROBUSTLY ESTIMATING DISPARITY FROM ADAPTIVE DENSE CURVES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (November 2003): 1081–109. http://dx.doi.org/10.1142/s0218001403002836.

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In this paper, we propose a novel area-based algorithm for stereo correspondence estimation. The new dense correspondence estimation algorithm, which we call HACM (Hierarchical Adaptive Curve Matching), is suitable for stereo analysis of both static and dynamic scenes. The core of this method is the derivation of a pixel-based dense adaptive curve representation. For each pixel in the concerned 2-D images, the changes are characterized roughly in the local surface shape, the texture direction, and the luminance properties, all at a slight overhead cost. The matching is carried out in a hierarchical manner that incorporates several novel ideas that improve its efficiency and robustness. Experiments conducted on both synthetic and natural stereo pairs, and natural video streams, reveal the favorable performance of this new algorithm.

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Ondrašovič, Milan, and Peter Tarábek. "Homography Ranking Based on Multiple Groups of Point Correspondences." Sensors 21, no. 17 (August 26, 2021): 5752. http://dx.doi.org/10.3390/s21175752.

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Homography mapping is often exploited to remove perspective distortion in images and can be estimated using point correspondences of a known object (marker). We focus on scenarios with multiple markers placed on the same plane if their relative positions in the world are unknown, causing an indeterminate point correspondence. Existing approaches may only estimate an isolated homography for each marker and cannot determine which homography achieves the best reprojection over the entire image. We thus propose a method to rank isolated homographies obtained from multiple distinct markers to select the best homography. This method extends existing approaches in the post-processing stage, provided that the point correspondences are available and that the markers differ only by similarity transformation after rectification. We demonstrate the robustness of our method using a synthetic dataset and show an approximately 60% relative improvement over the random selection strategy based on the homography estimation from the OpenCV library.

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